An image is comprised of foreground and background, and the foreground is usually a salient region of the image. In practical application process, the significant region which is also called salient region or salient object formally should be extracted from the image.
At present, many applications usually take advantage of the pixels' RGB value and its position to extract salient objects from an image which always introduced mistakes into the result. We just take one method as example. Firstly, a given image is segmented into superpixels which are then taken as input to the pre-trained convolutional neural network to extract feature for each superpixel. Secondly, a fully connected neural network is proposed to score each superpixel by virtue of the extracted features. Finally, a salient object can be popped out from the given image by virtue of merging the scored superpixels. In the above process, it takes a lot of time to segment the original image, and also takes lots of time to process each superpixel, which leads to the low efficacy of extracting salient object from original image.